日本地球惑星科学連合2025年大会
セッション概要| 固体地球科学 (S) | ||||
|---|---|---|---|---|
| セッション小記号 | 地震学 (SS) | |||
| セッション ID | S-SS06 | |||
| タイトル | New trends in data acquisition, analysis and interpretation of seismicity | |||
| タイトル短縮名 | New trends in seismology | |||
| 開催日時 | 口頭セッション | 5/30(金) PM1-PM2 |
現地口頭会場 | 301B |
| 現地 ポスター コアタイム |
5/30(金) PM3 | 現地ポスター会場 | 展示場ホール7・8 | |
| 代表コンビーナ | 氏名 | Enescu Bogdan | ||
| 所属 | 京都大学 大学院 理学研究科 地球惑星科学専攻 地球物理学教室 | |||
| 共同コンビーナ１ | 氏名 | Francesco Grigoli | ||
| 所属 | University of Pisa | |||
| 共同コンビーナ２ | 氏名 | 青木 陽介 | ||
| 所属 | 東京大学地震研究所 | |||
| 共同コンビーナ３ | 氏名 | 内出 崇彦 | ||
| 所属 | 産業技術総合研究所 地質調査総合センター 活断層・火山研究部門 | |||
| セッション言語 | E | |||
| スコープ |
In the last two decades, the number of high-quality seismic instruments installed worldwide has grown exponentially and likely will continue to grow in the coming decades, producing larger and larger datasets. This dramatic increase in the volume of available seismic data is partially due to the rising popularity of new technologies for seismic data acquisition based on fiber optics, characterized by an extremely high spatial and temporal sampling. Such systems are making seismological datasets grow in size and variety at an exceptionally fast rate, pushing the limit of current data analysis techniques. This data explosion, combined with new data analysis paradigms, including AI-based methods, is opening new research horizons in Seismology and related fields. Exploiting the massive amount of data is a challenge that can be overcome by adopting new approaches for seismic data analysis that can lead to enhanced seismic catalogs that can be used in conjunction with advanced statistical or physics-based methods to forecast seismicity or to correlate the seismic activity with other geophysical processes, including stress changes and migration of fluids in the crust or aseismic processes. This session aims to bring to light new methods for the analysis (either offline or in real-time) and quantitative interpretation of seismicity datasets collected across different scales and environments or with new seismic data acquisition technologies, such as fiber-optics-based sensors. Relevant topics to be presented include but are not limited to methods for seismicity characterization, statistical analysis of seismicity patterns in the space-time-magnitude domain, modeling and forecasting of seismicity, earthquake triggering and case studies. We thus encourage contributions that demonstrate how the proposed methods or the analysis of large datasets help to improve our understanding of earthquake and/or volcanic processes. |
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| 発表方法 | 口頭およびポスターセッション | |||
| 共催情報 | 学協会 | - | ||
| ジョイント | - | |||
| 団体会員以外の組織との共催 | - | |||
| 国際連携団体 | - | |||
| 招待講演 | ||||
| 時間 | 講演番号 | タイトル | 発表者 |
|---|---|---|---|
| 口頭発表 5月30日 PM1 | |||
| 13:45 - 14:00 | SSS06-01 | Machine Learning and Deep Learning Predicts Meter-Scale Laboratory Earthquakes | 乘杉 玲壽 |
| 14:00 - 14:15 | SSS06-02 | Noise Attenuation in Distributed Fiber-Optic Sensing Data Using a Spectral Subtraction-based Approach | Giulio Pascucci |
| 14:15 - 14:30 | SSS06-03 | Event location by waveform stacking using integrated fiber optic-seismometer networks | Emanuele Bozzi |
| 14:30 - 14:45 | SSS06-04 | Joint earthquake source inversion method using P-wave spectra and focal mechanism solutions | Yifang Cheng |
| 14:45 - 15:00 | SSS06-05 | Information Content of Earthquake Catalogs | John B Rundle |
| 15:00 - 15:15 | SSS06-06 | 情報理論に基づく地震予測可能性の定量化およびETASモデルの予測能力 | 庄 建倉 |
| 口頭発表 5月30日 PM2 | |||
| 15:31 - 15:46 | SSS06-07 | Inter-event time statistics for volcanic earthquakes | 波多野 恭弘 |
| 15:46 - 16:01 | SSS06-08 | Shallow rupture processes accompanying uplift at Campi Flegrei, Italy, revealed by moment tensor inversion and waveform clustering | Giacomo Rapagnani |
| 16:01 - 16:16 | SSS06-09 | Unsupervised exploration of seismic activity at Mount Fuji, Japan | Adele Doucet |
| 16:16 - 16:31 | SSS06-10 | Multi-array experiment of seismic monitoring at Merapi volcano using low-cost seismometers at sub-optimal distances | Theodorus Permana |
| 16:31 - 16:46 | SSS06-11 | Exploring the eruption sequence of the Klyuchevskoy volcano group and Shiveluch volcano (Kamchatka) in 2022-2023 with the seismic background level (SBL) technique | Nataliya Galina |
| 16:46 - 17:01 | SSS06-12 | Detection and location of seismovolcanic signals at Mutnovsky and Gorely volcanoes using seismic network-based methods | Yaroslav Berezhnev |
| 講演番号 | タイトル | 発表者 |
|---|---|---|
| ポスター発表 5月30日 PM3 | ||
| SSS06-P01 | Mutsu Bay Seismic Cluster along the Volcanic Front in November 2024 | 佐脇 泰典 |
| SSS06-P02 | Deep fault geometries in the Hidaka collision zone in southern Hokkaido revealed by hierarchical clustering of hypocenter distributions. | Admore Mpuang |
| SSS06-P03 | The determination of the seismic sequence characteristics and post-earthquake trend of the Ms6.4 earthquake in Yangbi, Yunnan,China on May 21, 2021 | Bateer Wu |
| SSS06-P04 | Determination of focal mechanism of earthquakes in and around the Korean Peninsula using P-wave first-motion polarity analysis based on deep learning | Mikyung Choi |
| SSS06-P05 | Integrating Advanced Phase Pickers and Graph Neural Networks for Seismic Phase Association in Türkiye | Nurcan Meral Ozel |
| SSS06-P06 | Machine Learning-based Surface Wave Analysis for High-Resolution Seismic Imaging in North China | Lu Dan |